Envelope-based partial least squares in functional regression

TL;DR

This paper introduces an envelope-based partial least squares method for functional regression, extending predictor envelope models to multivariate and functional data with theoretical guarantees and strong empirical performance.

Contribution

It develops a novel two-step estimation strategy for functional predictors in envelope models, including asymptotic theory and applicability to generalized linear models.

Findings

Estimator is root-n consistent and asymptotically normal

Method performs well in simulations and real data analyses

Effective across various outcome types and model settings

Abstract

In this article, we extend predictor envelope models to settings with multivariate outcomes and multiple, functional predictors. We propose a two-step estimation strategy, which first projects the function onto a finite-dimensional Euclidean space before fitting the model using existing approaches to envelope models. We first develop an estimator under a linear model with continuous outcomes and then extend this procedure to the more general class of generalized linear models, which allow for a variety of outcome types. We provide asymptotic theory for these estimators showing that they are root-$n$ consistent and asymptotically normal when the regression coefficient is finite-rank. Additionally we show that consistency can be obtained even when the regression coefficient has rank that grows with the sample size. Extensive simulation studies confirm our theoretical results and show strong prediction performance of the proposed estimators. Additionally, we provide multiple data analyses showing that the proposed approach performs well in real-world settings under a variety of outcome types compared with existing dimension reduction approaches.